Research Article
BibTex RIS Cite

Destek Vektör Makineleri ve M5 Karar Ağacı Yöntemleri Kullanılarak Yağış Akış İlişkisinin Tahmini

Year 2019, Volume: 10 Issue: 3, 1113 - 1124, 29.09.2019

Mustafa Demirci

https://doi.org/10.24012/dumf.525658
Cited By: 8

Abstract

Havza yönetimi ve afetlerin engellenmesi, su kaynaklarının daha verimli kullanılması ve su yapılarının inşasının planlaması amacı ile yağış ve akış verilerinin tahmini büyük önem taşımaktadır. Bu çalışmada Amerika Birleşik Devletleri Waltham Massachusetts'de yer alan Stony Brook rezervuarını besleyen Stony Brook nehrindeki 731 günlük yağış, akış ve sıcaklık bilgilerini içeren veriler kullanılarak modeller oluşturulmuştur. Bu veriler Destek Vektör Makineleri (SVM) ve M5 Karar Ağacı (M5T) yöntemlerinde girdi olarak kullanılmış ve yağış akış ilişkişi tahmin edilmiştir. Her iki yöntemle elde edilen sonuçlar gerçek ölçüm sonuçları ile karşılaştırılmaları yapılmıştır. Bunun sonucunda M5 Karar Ağacı (M5T) modellerinin akış tahmininde daha iyi performansa sahip olduğu görülmüştür.

Keywords

Yağış-Akış Tahmin Destek Vektör Makineleri M5 Karar Ağacı

References

  • Antonanzas, J., Urraca, R., Martinez-de-Pison, F. J., Antonanzas-Torres, F. (2015). Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy conversion and management, 100, 380-390.
  • Asefa, T., Kemblowski, M., Lall, U., Urroz, G. (2005). Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series. Water resources research, 41(12).
  • Bhattacharya, B., Solomatine, D. P. (2005). Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing, 63, 381-396.
  • Bray, M., Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6(4), 265-280.
  • Chen, H., Guo, J., Xiong, W., Guo, S., Xu, C. Y. (2010). Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 27(2), 274-284.
  • Cortes, C., Vapnik, V. (1995). Machine learning. Support vector networks, 20, 273-297.
  • Demirci M., Baltaci A. (2013), Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing and Applications, 23(1), 145-151.
  • Demirci M., Unes F., Saydemir S. (2015a), Suspended sediment estimation using an artificial intelligence approach. Sediment Matters, Springer, 83-95.
  • Demirci M., Unes F., Aköz M.S. (2015b), Prediction of cross-shore sandbar volumes using neural network approach. Journal of Marine Science and Technology, 20(1), 171-179.
  • Demirci M., Unes F., Akoz M.S. (2016), Determination of nearshore sandbar crest depth using neural network approach. International Journal of Advanced Engineering Research and Science, 3(12), 133-140.
  • Üneş, F., Demirci, M., Ispir, E., Kaya, Y. Z., Mamak, M., Tasar, B. (2017). Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey. In Environmental Engineering. Proceedings of the International Conference on Environmental Engineering. ICEE (Vol. 10, pp. 1-6). Vilnius Gediminas Technical University, Department of Construction Economics & Property.
  • Demirci M., Tasar B., Kaya Y.Z., (2018), Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models. Int. J. Adv. Eng. Res. Sci. 5, 206–211. doi:10.22161/ijaers.5.12.29
  • Demirci, M., Unes, F., Kaya, Y. Z., Tasar, B., Varcin, H. (2018). MODELING OF DAM RESERVOIR VOLUME USING ADAPTIVE NEURO FUZZY METHOD. Aerul si Apa. Componente ale Mediului, 145-152.
  • Dibike, Y. B., Velickov, S., Solomatine, D., Abbott, M. B. (2001). Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208-216.
  • Ergezer, H., Dikmen, M., Özdemir, E. (2003). Yapay sinir ağları ve tanıma sistemleri. PiVOLKA, 2(6), 14-17.
  • Fernando, D. A. K., Jayawardena, A. W. (1998). Runoff forecasting using RBF networks with OLS algorithm. Journal of hydrologic engineering, 3(3), 203-209.
  • Hosseini, S. M., Mahjouri, N. (2016). Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Applied Soft Computing, 38, 329-345.
  • Hsu, C. W., Chang, C. C. Lin, C. J. (2003) A practical guide to support vector classification. Tech. Report, Dept Computer Sci. & Info. Engng, National Taiwan University, Taiwan, China.
  • Hsu, K. L., Gupta, H. V., Sorooshian, S. (1995). Artificial neural network modeling of the rainfall‐runoff process. Water resources research, 31(10), 2517-2530.
  • Kaya, Y. Z., Mamak, M., Unes, F. (2016). Evapotranspiration Prediction Using M5T Data Mining Method. International Journal of Advanced Engineering Research and Science, 3(12).
  • Kaya, Y. Z., Üneş, F., Demirci, M., Taşar, B., Varçin, H. (2018). Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models. Aerul si Apa. Componente ale Mediului, 195-201.
  • Khan, M. S., Coulibaly, P. (2006). Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering, 11(3), 199-205.
  • Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T., Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29(1), 72-88.
  • Mason, J. C., Price, R. K., Tem'Me, A. (1996). A neural network model of rainfall-runoff using radial basis functions. Journal of Hydraulic Research, 34(4), 537-548.
  • McCulloch, W. S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
  • Minns, A. W., Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological sciences journal, 41(3), 399-417.
  • Nieto, P. G., Torres, J. M., Fernández, M. A., Galán, C. O. (2012). Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematical Modelling, 36(12), 6137-6145.
  • Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence 92, 343-348.
  • Radhika, Y., Shashi, M. (2009). Atmospheric temperature prediction using support vector machines. International Journal of Computer Theory and Engineering, 1(1), 55.
  • Rao, M., Fan, G., Thomas, J., Cherian, G., Chudiwale, V., Awawdeh, M. (2007). A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP). Environmental Modelling & Software, 22(9), 1270-1280.
  • Sattari, M. T., Pal, M., Apaydin, H., Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), 233-242.
  • Solomatine, D. P., Xue, Y. (2004). M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrologic Engineering, 9(6), 491-501.
  • Tasar B., Unes F., Demirci M., Kaya Y.Z. (2018), Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 9(1), 543-551.
  • Tasar B., Kaya Y. Z., Varçin H., Unes F., Demirci M. (2017), Forecasting of suspended sediment in rivers using artificial neural networks approach. International Journal of Advanced Engineering Research and Science, 4(12), 79-84.
  • Tripathi, S., Srinivas, V. V., Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330(3-4), 621-640.
  • Turhan, E., 2012. Seyhan Havzası’nın Yağış- Akış İlişkisinin Yapay Sinir Ağları Yöntemi ile Modellenmesi, Yüksek Lisans Tezi, Adana.
  • Unes F. (2010a), Dam reservoir level modelıng by neural network approach: a case study. Neural Network World, 4(10), 461.
  • Unes F. (2010b), Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach. Clean–Soil, Air, Water, 38(3), 296-308.
  • Unes F., Demirci M. (2015), Generalized Regression Neural Networks For Reservoir Level Modeling. International Journal of Advanced Computational Engineering and Networking, 3, 81- 84.
  • Unes F., Yildirim S., Cigizoglu H.K., Coskun H. (2013), Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression. Journal of Engineering Research, 1(3), 53-74.
  • Unes F., Demirci M., Kişi Ö. (2015), Prediction of millers ferry dam reservoir level in USA using artificial neural network. Periodica Polytechnica Civil Engineering, 59(3), 309–318.
  • Unes F., Gumuscan F.G., Demirci M. (2017), Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. EJENS, 2(1), 144-148.
  • Üneş F., Demirci M., Mertcan Z., Taşar B., Varçin H., Ziya Y. (2018a). Determination of Groundwater Level Fluctuations by Artificial Neural Networks . Natural and Engineering Sciences, 3(3), Supplement, 35-42.
  • Üneş F., Doğan S., Taşar B., Kaya Y., Demirci M. (2018b), The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods. Natural and Engineering Sciences, 3(3), Supplement, 54-64.
  • Unes F., Bölük O., Kaya Y. Z., Tasar B., Varçin H. (2018c), Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin. Journal of Engineering Research.
  • USGS.gov | Science for a changing world [WWW Document], n.d. URL https://www.usgs.gov/
  • Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995.
  • Vapnik, V., Golowich, S., Smola, A. (1997) Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, pp. 281–287.
  • Witten, I.H., Frank, E. (2005). Data mining: Practical machine learning tools and techniques with java implementations, Morgan Kaufmann, San Francisco, CA.
  • Yu, P. S., Chen, S. T., Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.

Year 2019, Volume: 10 Issue: 3, 1113 - 1124, 29.09.2019

Mustafa Demirci

https://doi.org/10.24012/dumf.525658
Cited By: 8

Abstract

References

  • Antonanzas, J., Urraca, R., Martinez-de-Pison, F. J., Antonanzas-Torres, F. (2015). Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy conversion and management, 100, 380-390.
  • Asefa, T., Kemblowski, M., Lall, U., Urroz, G. (2005). Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series. Water resources research, 41(12).
  • Bhattacharya, B., Solomatine, D. P. (2005). Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing, 63, 381-396.
  • Bray, M., Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6(4), 265-280.
  • Chen, H., Guo, J., Xiong, W., Guo, S., Xu, C. Y. (2010). Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 27(2), 274-284.
  • Cortes, C., Vapnik, V. (1995). Machine learning. Support vector networks, 20, 273-297.
  • Demirci M., Baltaci A. (2013), Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing and Applications, 23(1), 145-151.
  • Demirci M., Unes F., Saydemir S. (2015a), Suspended sediment estimation using an artificial intelligence approach. Sediment Matters, Springer, 83-95.
  • Demirci M., Unes F., Aköz M.S. (2015b), Prediction of cross-shore sandbar volumes using neural network approach. Journal of Marine Science and Technology, 20(1), 171-179.
  • Demirci M., Unes F., Akoz M.S. (2016), Determination of nearshore sandbar crest depth using neural network approach. International Journal of Advanced Engineering Research and Science, 3(12), 133-140.
  • Üneş, F., Demirci, M., Ispir, E., Kaya, Y. Z., Mamak, M., Tasar, B. (2017). Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey. In Environmental Engineering. Proceedings of the International Conference on Environmental Engineering. ICEE (Vol. 10, pp. 1-6). Vilnius Gediminas Technical University, Department of Construction Economics & Property.
  • Demirci M., Tasar B., Kaya Y.Z., (2018), Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models. Int. J. Adv. Eng. Res. Sci. 5, 206–211. doi:10.22161/ijaers.5.12.29
  • Demirci, M., Unes, F., Kaya, Y. Z., Tasar, B., Varcin, H. (2018). MODELING OF DAM RESERVOIR VOLUME USING ADAPTIVE NEURO FUZZY METHOD. Aerul si Apa. Componente ale Mediului, 145-152.
  • Dibike, Y. B., Velickov, S., Solomatine, D., Abbott, M. B. (2001). Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208-216.
  • Ergezer, H., Dikmen, M., Özdemir, E. (2003). Yapay sinir ağları ve tanıma sistemleri. PiVOLKA, 2(6), 14-17.
  • Fernando, D. A. K., Jayawardena, A. W. (1998). Runoff forecasting using RBF networks with OLS algorithm. Journal of hydrologic engineering, 3(3), 203-209.
  • Hosseini, S. M., Mahjouri, N. (2016). Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Applied Soft Computing, 38, 329-345.
  • Hsu, C. W., Chang, C. C. Lin, C. J. (2003) A practical guide to support vector classification. Tech. Report, Dept Computer Sci. & Info. Engng, National Taiwan University, Taiwan, China.
  • Hsu, K. L., Gupta, H. V., Sorooshian, S. (1995). Artificial neural network modeling of the rainfall‐runoff process. Water resources research, 31(10), 2517-2530.
  • Kaya, Y. Z., Mamak, M., Unes, F. (2016). Evapotranspiration Prediction Using M5T Data Mining Method. International Journal of Advanced Engineering Research and Science, 3(12).
  • Kaya, Y. Z., Üneş, F., Demirci, M., Taşar, B., Varçin, H. (2018). Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models. Aerul si Apa. Componente ale Mediului, 195-201.
  • Khan, M. S., Coulibaly, P. (2006). Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering, 11(3), 199-205.
  • Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T., Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29(1), 72-88.
  • Mason, J. C., Price, R. K., Tem'Me, A. (1996). A neural network model of rainfall-runoff using radial basis functions. Journal of Hydraulic Research, 34(4), 537-548.
  • McCulloch, W. S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
  • Minns, A. W., Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological sciences journal, 41(3), 399-417.
  • Nieto, P. G., Torres, J. M., Fernández, M. A., Galán, C. O. (2012). Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Applied Mathematical Modelling, 36(12), 6137-6145.
  • Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence 92, 343-348.
  • Radhika, Y., Shashi, M. (2009). Atmospheric temperature prediction using support vector machines. International Journal of Computer Theory and Engineering, 1(1), 55.
  • Rao, M., Fan, G., Thomas, J., Cherian, G., Chudiwale, V., Awawdeh, M. (2007). A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP). Environmental Modelling & Software, 22(9), 1270-1280.
  • Sattari, M. T., Pal, M., Apaydin, H., Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), 233-242.
  • Solomatine, D. P., Xue, Y. (2004). M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrologic Engineering, 9(6), 491-501.
  • Tasar B., Unes F., Demirci M., Kaya Y.Z. (2018), Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 9(1), 543-551.
  • Tasar B., Kaya Y. Z., Varçin H., Unes F., Demirci M. (2017), Forecasting of suspended sediment in rivers using artificial neural networks approach. International Journal of Advanced Engineering Research and Science, 4(12), 79-84.
  • Tripathi, S., Srinivas, V. V., Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330(3-4), 621-640.
  • Turhan, E., 2012. Seyhan Havzası’nın Yağış- Akış İlişkisinin Yapay Sinir Ağları Yöntemi ile Modellenmesi, Yüksek Lisans Tezi, Adana.
  • Unes F. (2010a), Dam reservoir level modelıng by neural network approach: a case study. Neural Network World, 4(10), 461.
  • Unes F. (2010b), Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach. Clean–Soil, Air, Water, 38(3), 296-308.
  • Unes F., Demirci M. (2015), Generalized Regression Neural Networks For Reservoir Level Modeling. International Journal of Advanced Computational Engineering and Networking, 3, 81- 84.
  • Unes F., Yildirim S., Cigizoglu H.K., Coskun H. (2013), Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression. Journal of Engineering Research, 1(3), 53-74.
  • Unes F., Demirci M., Kişi Ö. (2015), Prediction of millers ferry dam reservoir level in USA using artificial neural network. Periodica Polytechnica Civil Engineering, 59(3), 309–318.
  • Unes F., Gumuscan F.G., Demirci M. (2017), Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. EJENS, 2(1), 144-148.
  • Üneş F., Demirci M., Mertcan Z., Taşar B., Varçin H., Ziya Y. (2018a). Determination of Groundwater Level Fluctuations by Artificial Neural Networks . Natural and Engineering Sciences, 3(3), Supplement, 35-42.
  • Üneş F., Doğan S., Taşar B., Kaya Y., Demirci M. (2018b), The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods. Natural and Engineering Sciences, 3(3), Supplement, 54-64.
  • Unes F., Bölük O., Kaya Y. Z., Tasar B., Varçin H. (2018c), Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin. Journal of Engineering Research.
  • USGS.gov | Science for a changing world [WWW Document], n.d. URL https://www.usgs.gov/
  • Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995.
  • Vapnik, V., Golowich, S., Smola, A. (1997) Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, pp. 281–287.
  • Witten, I.H., Frank, E. (2005). Data mining: Practical machine learning tools and techniques with java implementations, Morgan Kaufmann, San Francisco, CA.
  • Yu, P. S., Chen, S. T., Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
There are 50 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Mustafa Demirci 0000-0002-3249-2586

Publication Date September 29, 2019
Submission Date February 11, 2019
Published in Issue Year 2019 Volume: 10 Issue: 3

Cite

IEEE M. Demirci, “Destek Vektör Makineleri ve M5 Karar Ağacı Yöntemleri Kullanılarak Yağış Akış İlişkisinin Tahmini”, DUJE, vol. 10, no. 3, pp. 1113–1124, 2019, doi: 10.24012/dumf.525658.

Cited By

KENT ÖLÇEĞİNDE TAŞINMAZ DEĞER DEĞİŞİMİNİN MAKİNE ÖĞRENİM ALGORİTMALARI YARDIMIYLA ANALİZİ
Türkiye Arazi Yönetimi Dergisi
https://doi.org/10.51765/tayod.1275671
Derin Öğrenme ve Makine Öğrenmesi Yöntemleriyle Polisomnografi Sinyallerinden Uyku Evrelerinin Sınıflandırılması
Karadeniz Fen Bilimleri Dergisi
https://doi.org/10.31466/kfbd.1246482
Bilecik Merkez Bölgesindeki Mermer Ocaklarının Arazi Değişimi ve Arazi Örtüsü Değişiminin Destek Vektör Makine Yöntemi İle Belirlenmesi
Black Sea Journal of Engineering and Science
https://doi.org/10.34248/bsengineering.1246273
Finansal Tablolarda Hile Riskinin Tespit Edilmesinde Veri Madenciliği Yöntemlerinin Kullanılmasına Yönelik Bir Araştırma
Journal of Yaşar University
https://doi.org/10.19168/jyasar.956623
Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall
Engineering Applications of Computational Fluid Mechanics
https://doi.org/10.1080/19942060.2020.1803971
Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey
Climate Research
https://doi.org/10.3354/cr01612
Machine Learning Based Segmentation of Shoreline Using Mean-Shift, Random Forest and Support Vector Machine
Çukurova Üniversitesi Mühendislik Fakültesi Dergisi
https://doi.org/10.21605/cukurovaumfd.1190597
Model-Ağacı Yaklaşımını Kullanarak Türkiye’de HELIOSAT Tabanlı Güneş Radyasyonunun Modellenmesi
Geomatik
https://doi.org/10.29128/geomatik.1137687
 Download Cover Image
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456